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Forecasting Government Size in Iran Using Artificial Neural Network

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  • Ahmad Jafari Samimi

Abstract

In this study, artificial neural network (ANN) for forecasting government size in Iran is applied. The purpose of the study is comparison various architectures, transfer functions and learning algorithms on the operation of network, for this purpose the annual data from 1971-2007 of selected variable are used. Variables are tax income, oil revenue, population, openness, government expenditure, GDP and GDP per capita; these variables are selected based on economic theories. Result shows that networks with various training algorithms and transfer functions have different results. Best architecture is a network with two hidden layer and twelve (12) neuron in hidden layers with hyperbolic tangent transfer function both in hidden and output layers with Quasi -Newton training algorithm. Base on findings in this study suggested in using neural network must be careful in selecting the architecture, transfer function and training algorithms.

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  • Ahmad Jafari Samimi, 2011. "Forecasting Government Size in Iran Using Artificial Neural Network," Journal of Economics and Behavioral Studies, AMH International, vol. 3(5), pages 274-278.
    • Handle: RePEc:rnd:arjebs:v:3:y:2011:i:5:p:274-278
    DOI: 10.22610/jebs.v3i5.280
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